Traffic prediction is a useful tool to help in the management of overall traffic control. Highly accurate traffic predictions have the potential to improve traffic conditions, reduce travel delays, and make roadways more efficient. There are multiple methods that have been implemented in predicting the traffic at any given time of day, including algorithms that use historic traffic data, real time traffic data, or a combination of both. The traffic data is typically gathered using traffic sensors, embedded in a road, that are capable of measuring traffic flow for many road segments, or links, in a transportation network.
Various sets of parameters have been used to better estimate the real-world conditions of traffic models, for example, what effects links have on each other in a road network and how to better represent the traffic flow at peak and off-peak hours. As a result, methods for calculating traffic predictions have emerged which take into account both the spatial aspects and temporal correlations of roadways. When performing these high level calculations, there is often a balance to achieve between the complexity of the computational calculation and the accuracy of the calculation.
It is advantageous to travelers to have knowledge of the current conditions of traffic on a particular roadway, as well as possible future conditions. Efforts have been implemented to deal with traffic congestion in various ways, such as obtaining information about current traffic conditions and estimating and providing information about possible future traffic conditions.
Vehicular traffic can be predicted using real-time data, such as data gathered from traffic sensors in the road, the Department of Transportation in a city, or a state highway patrol, among other agencies. More recently, time series models such as the Auto-Regressive Integrated Moving Average (ARIMA) and the Neural Network (NNet) models have been used. These algorithms can be useful in predicting short-term traffic; however, they do not consider events that may occur in real-time traffic, such as speed changes during rush hour.
One current solution uses a small time scale, such as collection times of 5 minutes as opposed to 15 minutes, to attempt to achieve a greater accuracy in prediction calculations. Here, the parameters used were selected to attempt to better estimate a broader range of real-time roadways at any time during the week, and are run continuously to provide more accurate prediction results. However, the parameters used in this algorithm must initially be calibrated to a certain degree of accuracy before they can be used in the traffic prediction calculations.